{"ID":2874029,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.05809","arxiv_id":"2509.05809","title":"A Probabilistic Segment Anything Model for Ambiguity-Aware Medical Image Segmentation","abstract":"Recent advances in promptable segmentation, such as the Segment Anything Model (SAM), have enabled flexible, high-quality mask generation across a wide range of visual domains. However, SAM and similar models remain fundamentally deterministic, producing a single segmentation per object per prompt, and fail to capture the inherent ambiguity present in many real-world tasks. This limitation is particularly troublesome in medical imaging, where multiple plausible segmentations may exist due to annotation uncertainty or inter-expert variability. In this paper, we introduce Probabilistic SAM, a probabilistic extension of SAM that models a distribution over segmentations conditioned on both the input image and prompt. By incorporating a latent variable space and training with a variational objective, our model learns to generate diverse and plausible segmentation masks reflecting the variability in human annotations. The architecture integrates a prior and posterior network into the SAM framework, allowing latent codes to modulate the prompt embeddings during inference. The latent space allows for efficient sampling during inference, enabling uncertainty-aware outputs with minimal overhead. We evaluate Probabilistic SAM on the public LIDC-IDRI lung nodule dataset and demonstrate its ability to produce diverse outputs that align with expert disagreement, outperforming existing probabilistic baselines on uncertainty-aware metrics. Our code is available at: https://github.com/tbwa233/Probabilistic-SAM/.","short_abstract":"Recent advances in promptable segmentation, such as the Segment Anything Model (SAM), have enabled flexible, high-quality mask generation across a wide range of visual domains. However, SAM and similar models remain fundamentally deterministic, producing a single segmentation per object per prompt, and fail to capture...","url_abs":"https://arxiv.org/abs/2509.05809","url_pdf":"https://arxiv.org/pdf/2509.05809v1","authors":"[\"Tyler Ward\",\"Abdullah Imran\"]","published":"2025-09-06T19:02:53Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":610109,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2874029,"paper_url":"https://arxiv.org/abs/2509.05809","paper_title":"A Probabilistic Segment Anything Model for Ambiguity-Aware Medical Image Segmentation","repo_url":"https://github.com/tbwa233/Probabilistic-SAM","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}