A Warmer Start to Active Learning with Adaptive Gaussian Mixture Models for Skin Lesion Segmentation

Active learning is a promising strategy for reducing annotation burdens in medical image segmentation, particularly for tasks like skin lesion segmentation, where expert annotations are costly and time-intensive. However, existing methods suffer from cold-start issues and inefficient sample selection. This paper introduces a novel active learning framework called Task-Aligned Iterative Active Learning (TAIAL) that employs clustering and entropy ranking on a progressively refined feature space to select active samples that balance diversity, informativeness, and uncertainty. Coupled with a self-supervised initialization step, TAIAL provides an effective solution for both the cold-start problem and sample selection. Extensive experiments on the ISIC17 dataset demonstrate that TAIAL achieves early-stage sample selection performance, representing a 32% improvement over random sampling and an average improvement of 27% over other active learning schemes. In the later stage, it reaches 98.7% of fully supervised performance with only 38.4% labeled data, outperforming baseline methods. Our approach provides a scalable and efficient active learning paradigm for annotation-constrained medical imaging applications.