Recently, Segmenting Anything has taken an important step towards general artificial intelligence. At the same time, its reliability and fairness have also attracted great attention, especially in the field of health care. In this study, we propose multi-box prompts triggered uncertainty estimation for SAM cues to demonstrate the reliability of segmented lesions or tissues. We estimate the distribution of SAM predictions via Monte Carlo with prior distribution parameters, which employs different prompts as formulation of test-time augmentation. Our experimental results found that multi-box prompts augmentation improve the SAM performance, and endowed each pixel with uncertainty. This provides the first paradigm for a reliable SAM.

翻译：近年来，"分割一切"（Segmenting Anything）在通用人工智能领域迈出了重要一步。与此同时，其可靠性和公平性也引起了广泛关注，尤其是在医疗健康领域。本研究针对SAM模型提出了一种基于多框提示触发的不确定性估计方法，用于验证分割病灶或组织的可靠性。我们通过结合先验分布参数的蒙特卡洛方法估计SAM预测的分布，将不同提示作为测试时数据增强的具体形式。实验结果表明，多框提示增强不仅提升了SAM的分割性能，还为每个像素赋予了不确定性度量。这为构建可靠的SAM模型提供了首个范式依据。