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arXiv · 2604.15950

TwinTrack: Post-hoc Multi-Rater Calibration for Medical Image Segmentation

Tristan Kirscher·Alexandra Ertl·Klaus Maier-Hein·Xavier Coubez·Philippe Meyer·Sylvain Faisan
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Abstract

Pancreatic ductal adenocarcinoma (PDAC) segmentation on contrast-enhanced CT is inherently ambiguous: inter-rater disagreement among experts reflects genuine uncertainty rather than annotation noise. Standard deep learning approaches assume a single ground truth, producing probabilistic outputs that can be poorly calibrated and difficult to interpret under such ambiguity. We present TwinTrack, a framework that addresses this gap through post-hoc calibration of ensemble segmentation probabilities to the empirical mean human response (MHR) -the fraction of expert annotators labeling a voxel as tumor. Calibrated probabilities are thus directly interpretable as the expected proportion of annotators assigning the tumor label, explicitly modeling inter-rater disagreement. The proposed post-hoc calibration procedure is simple and requires only a small multi-rater calibration set. It consistently improves calibration metrics over standard approaches when evaluated on the MICCAI 2025 CURVAS-PDACVI multi-rater benchmark.

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Tristan Kirscher, Alexandra Ertl, Klaus Maier-Hein, Xavier Coubez, Philippe Meyer, Sylvain Faisan. 2026-05-18. TwinTrack: Post-hoc Multi-Rater Calibration for Medical Image Segmentation. https://arxiv.org/abs/2604.15950

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