Despite recent progress in text-prompt-based medical image segmentation, existing LISA-like MLLM-based methods typically generate masks regardless of whether the target specified in the query is present, leading to hallucinated segmentation. In this work, we propose MedVeriSeg, a training-free query verification framework that enables LISA-like medical segmentation models to reject false segmentation queries. MedVeriSeg first quantifies the response quality between the [SEG] token and image features through a Similarity Response Quality Scoring Module. To further improve robustness, it employs a Lightweight Routed Multi-Agent Verification Module, which fuses quantitative score evidence with qualitative agent evidence to comprehensively verify the validity of the query. To support systematic evaluation, we construct MedVeriSeg-Bench, a benchmark designed for query verification in medical image segmentation. Experimental results demonstrate that MedVeriSeg effectively identifies false segmentation queries and reduces hallucinated segmentation, while maintaining a high acceptance rate for valid queries, thereby largely preserving the segmentation utility of LISA-like medical segmentation models.

翻译：尽管文本提示的医学图像分割近期取得进展，现有基于LISA的多模态大语言模型方法通常无论查询中指定目标是否存在都生成掩码，导致幻觉分割。本文提出MedVeriSeg——一种无需训练的查询验证框架，使LISA类医学分割模型能够拒绝虚假分割查询。MedVeriSeg首先通过相似度响应质量评分模块量化[SEG]标记与图像特征之间的响应质量。为进一步提升鲁棒性，它采用轻量级路由多智能体验证模块，融合定量评分证据与定性智能体证据，综合验证查询有效性。为支持系统评估，我们构建了面向医学图像分割查询验证的基准数据集MedVeriSeg-Bench。实验结果表明，MedVeriSeg能有效识别虚假分割查询并减少幻觉分割，同时在保留有效查询时保持高接受率，从而大幅维持LISA类医学分割模型的分割效用。