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Principle-Guided Supervision for Interpretable Uncertainty in Medical Image Segmentation

Summary

This paper proposes a principle-guided uncertainty supervision framework (PriUS) based on evidential learning, which enforces three perception-aligned principles—image contrast, corruption severity, and geometric complexity—during training to improve the interpretability and consistency of uncertainty estimates in medical image segmentation. Experiments on ACDC, ISIC, and WHS datasets show PriUS achieves more consistent uncertainty estimates while maintaining competitive segmentation performance compared to state-of-the-art methods.

SourcearXiv Computer VisionAuthor: An Sui, Yuzhu Li, Gunter Schumann, Fuping Wu, Xiahai Zhuang
Principle-Guided Supervision for Interpretable Uncertainty in Medical Image Segmentation
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[2605.10984] Principle-Guided Supervision for Interpretable Uncertainty in Medical Image Segmentation

[Submitted on 9 May 2026]

Title:Principle-Guided Supervision for Interpretable Uncertainty in Medical Image Segmentation

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Abstract:Uncertainty quantification complements model predictions by characterizing their reliability, which is essential for high-stakes decision making such as medical image segmentation. However, most existing methods reduce uncertainty to a scalar confidence estimate, leaving its spatial distribution semantically underconstrained. In this work, we focus on uncertainty interpretability, namely, whether estimated uncertainty behaves in a human-understandable manner with respect to sources of ambiguity. We identify three perception-aligned principles requiring the spatial distribution of uncertainty to reflect: (1) image contrast between structures, (2) severity of image corruption, and (3) geometric complexity in anatomical structures. Accordingly, we develop a principle-guided uncertainty supervision framework (PriUS) based on evidential learning, in which the corresponding supervision objectives are explicitly enforced during training. We further introduce quantitative metrics to measure the consistency between predicted uncertainty and image attributes that induce ambiguity. Experiments on ACDC, ISIC, and WHS datasets showed that, compared with state-of-the-art methods, PriUS produced more consistent uncertainty estimates while maintaining competitive segmentation performance.

Comments: 14 pages, 8 figures

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2605.10984 [cs.CV]

(or arXiv:2605.10984v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2605.10984

arXiv-issued DOI via DataCite

Submission history

From: An Sui [view email] [v1] Sat, 9 May 2026 05:17:20 UTC (5,733 KB)

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Key points and analysis

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Key points

  • Identified three perception-aligned principles to constrain spatial uncertainty distribution.
  • Developed PriUS framework using evidential learning with explicit supervision objectives.
  • Introduced quantitative metrics to measure consistency between predicted uncertainty and image attributes.
  • Validated on multiple datasets, showing superior consistency and competitive segmentation performance.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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