arXiv Computer Vision October 9, 2026 By Zhuofan Xie, Zishan Lin, Jinliang Lin, Jie Qi, Shaohua Hong, Shuo Li

Similarity-as-Evidence: Calibrating Overconfident VLMs for Interpretable and Label-Efficient Medical Active Learning

Read the original on arXiv Computer Vision →

The paper introduces Similarity-as-Evidence (SaE), a framework that calibrates overconfident Vision‑Language Models (VLMs) for medical active learning by reinterpreting text‑image similarity vectors as evidence and modeling them with a Dirichlet distribution. SaE’s Similarity Evidence Head (SEH) captures uncertainty through vacuity and dissonance, enabling a dual‑factor acquisition strategy that prioritizes high‑vacuity samples early and high‑dissonance samples later. Experiments on ten public medical imaging datasets demonstrate state‑of‑the‑art macro‑averaged accuracy (82.57%) and improved calibration (NLL = 0.425) under a 20% label budget.

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