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

TL;DR

TwinTrack introduces a post-hoc calibration method for ensemble segmentation probabilities in ambiguous medical imaging, aligning them with actual inter-rater disagreement to improve interpretability.

Contribution

It presents a simple, effective post-hoc calibration framework that models inter-rater disagreement in medical image segmentation, enhancing probability calibration.

Findings

Improves calibration metrics over standard methods on the MICCAI 2025 benchmark.

Requires only a small multi-rater calibration set.

Explicitly models inter-rater disagreement as the expected annotator response.

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.