Abstract
Reliable uncertainty maps are needed when medical segmentations drive downstream measurements, triage, or clinical review. Strong uncertainty baselines often require repeated inference, while efficient single-forward-pass methods can be weak at ranking failures or depend on restrictive feature-space assumptions. We present SegWithU, a supervised frozen-backbone uncertainty module for medical image segmentation. SegWithU leaves a pretrained 3D segmentation backbone unchanged, taps decoder features, and learns a compact probe space whose deterministic signed perturbation patterns measure local prediction instability. The module produces two voxel-wise maps with distinct roles: a calibration map for probability tempering and a ranking map for error localization and selective prediction. We explicitly formulate the perturbation patterns, map dependencies, training losses, and voxel-level evaluation protocol. On ACDC, BraTS2024, and LiTS, SegWithU achieves AUROC/AURC of , , and , respectively, with AURC reported in units, while preserving the frozen backbone’s hard segmentations. Ablations show that the calibration/ranking decomposition, ranking losses, and learned perturbation probes each contribute to performance. Source code is available at https://github.com/ProjectNeura/SegWithU.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/UNSURE2026_040.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: https://papers.miccai.org/miccai-2026-sat/supp/UNSURE2026_040_supp.pdf
Link to Open Review
Open Review Page: https://openreview.net/profile?id=~Tianhao_Fu2
BibTex
@InProceedings{FuTia_SegWithU_MICCAISAT2026,
author = { Fu, Tianhao AND Wang, Austin AND Chen, Charles AND Aldave-Garza, Roby AND Chen, Yucheng},
title = { { SegWithU: Deterministic Perturbation Probes for Single-Backbone-Pass Risk-Aware Medical Image Segmentation } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17260},
month = {pending},
page = {pending}
}