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arXiv · 2605.10603

Segment Anything with Robust Uncertainty-Accuracy Correlation

Hongyou Zhou·Marc Toussaint·Ling Shao·Zihan Ye
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Abstract

Despite strong zero-shot performance, SAM is unreliable under domain shift due to Mask-level Confidence Confusion (MCC), where a single IoU-based mask score fails to reflect pixel-wise reliability near boundaries. Motivated by the contrast between texture-biased shortcuts in neural networks and shape-centric processing in human vision, we model out-of-domain variation as appearance shifts and non-rigid deformations that jointly stress calibration. We propose Segment Anything with Robust Uncertainty-Accuracy Correlation (RUAC) for robust pixel-wise uncertainty estimation under appearance and deformation shifts. RUAC adds a lightweight uncertainty head, trains it with a collaborative style-deformation attack that jointly perturbs texture and geometry, and applies Uncertainty-Accuracy Alignment to ensure uncertainty consistently highlights erroneous pixels even under adversarial perturbations. Across 23 zero-shot domains, RUAC improves segmentation quality and yields more faithful uncertainty with stronger uncertainty-accuracy correlation. Project page: https://hongyouzhou.github.io/ruac/.

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Hongyou Zhou, Marc Toussaint, Ling Shao, Zihan Ye. 2026-05-11. Segment Anything with Robust Uncertainty-Accuracy Correlation. https://arxiv.org/abs/2605.10603

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