Accurate segmentation annotations are critical for disease monitoring, yet manual labeling remains a major bottleneck due to the time and expertise required. Active learning (AL) alleviates this burden by prioritizing informative samples for annotation, typically through a diversity-based cold-start phase followed by uncertainty-driven selection. We propose a novel cold-start sampling strategy that combines foundation-model embeddings with clustering, including automatic selection of the number of clusters and proportional sampling across clusters, to construct a diverse and representative initial training. This is followed by an uncertainty-based AL framework that integrates spatial diversity to guide sample selection. The proposed method is intuitive and interpretable, enabling visualization of the feature-space distribution of candidate samples. We evaluate our approach on three datasets spanning X-ray and MRI modalities. On the CheXmask dataset, the cold-start strategy outperforms random selection, improving Dice from 0.918 to 0.929 and reducing the Hausdorff distance from 32.41 to 27.66 mm. In the AL setting, combined entropy and diversity selection improves Dice from 0.919 to 0.939 and reduces the Hausdorff distance from 30.10 to 19.16 mm. On the Montgomery dataset, cold-start gains are substantial, with Dice improving from 0.928 to 0.950 and Hausdorff distance decreasing from 14.22 to 9.38 mm. On the SynthStrip dataset, cold-start selection slightly affects Dice but reduces the Hausdorff distance from 9.43 to 8.69 mm, while active learning improves Dice from 0.816 to 0.826 and reduces the Hausdorff distance from 7.76 to 6.38 mm. Overall, the proposed framework consistently outperforms baseline methods in low-data regimes, improving segmentation accuracy.

翻译：精确的分割标注对于疾病监测至关重要，然而由于所需的时间和专业知识，手动标注仍然是主要的瓶颈。主动学习通过优先标注信息量丰富的样本来减轻这一负担，通常包括一个基于多样性的冷启动阶段，随后是不确定性驱动的选择。我们提出了一种新颖的冷启动采样策略，该策略将基础模型嵌入与聚类相结合，包括自动选择聚类数量以及在聚类间进行比例采样，以构建一个多样且具有代表性的初始训练集。随后是一个基于不确定性的主动学习框架，该框架整合了空间多样性来指导样本选择。所提出的方法直观且可解释，能够可视化候选样本在特征空间中的分布。我们在涵盖X射线和MRI模态的三个数据集上评估了我们的方法。在CheXmask数据集上，冷启动策略优于随机选择，将Dice系数从0.918提升至0.929，并将豪斯多夫距离从32.41毫米降低至27.66毫米。在主动学习设置中，结合熵和多样性的选择将Dice系数从0.919提升至0.939，并将豪斯多夫距离从30.10毫米降低至19.16毫米。在Montgomery数据集上，冷启动带来的增益显著，Dice系数从0.928提升至0.950，豪斯多夫距离从14.22毫米降低至9.38毫米。在SynthStrip数据集上，冷启动选择对Dice系数影响较小，但将豪斯多夫距离从9.43毫米降低至8.69毫米，而主动学习将Dice系数从0.816提升至0.826，并将豪斯多夫距离从7.76毫米降低至6.38毫米。总体而言，所提出的框架在低数据量情况下始终优于基线方法，提高了分割准确性。