SEQUAL: Self-refining and effective querying active learning with pseudo label divergence score for carotid intima-media segmentation in ultrasound

Deep learning has achieved remarkable performance in carotid intima-media (CIM) segmentation from ultrasound images, but its clinical applicability remains limited due to data scarcity, annotation variability, and low image quality. While active learning (AL) aims to minimize labeling cost while model learning, the conventional AL approaches do not fully support sparse and noisy clinical labels that commonly occur in real-world application of CIM segmentation. In this paper, we propose Self-refining and Effective QUerying Active Learning (SEQUAL), a novel AL framework tailored for CIM segmentation under sparse and noisy supervision. SEQUAL introduces a self-refinement mechanism that leverages high-confidence pseudo-labels generated by the model and fuses them with sparse clinical annotations, enabling progressive enhancement of both label quality and model predictions. For efficient and targeted annotation, SEQUAL also proposes a new query strategy based on the Pseudo Label Divergence (PLD) score, which quantifies the information gain introduced by self-refinement. A dual-network design enables fast PLD computation, selecting the most informative samples for annotation and accelerating model convergence. Extensive experiments on carotid ultrasound datasets show that SEQUAL consistently outperforms conventional AL methods in segmentation accuracy, annotation efficiency, and robustness. Moreover, pilot clinical studies demonstrate that CIM measurements derived from SEQUAL’s predictions are consistent with expert assessments, confirming its practical utility. The code will be released upon acceptance at https://github.com/yucheng722/SEQUAL .