Principle-Guided Supervision for Interpretable Uncertainty in Medical Image Segmentation

TL;DR

This paper introduces PriUS, a framework that guides uncertainty estimation in medical image segmentation to be more interpretable and aligned with human perception, improving consistency without sacrificing accuracy.

Contribution

The authors propose a principle-guided supervision framework for uncertainty in segmentation that enforces interpretability aligned with image attributes, a novel approach in the field.

Findings

PriUS produces more consistent uncertainty estimates than state-of-the-art methods.

PriUS maintains competitive segmentation performance.

Quantitative metrics confirm improved alignment of uncertainty with image ambiguity.

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.