SPDA: Efficient Online Test-Time Adaptation for Promptable Medical Segmentation

Promptable foundation segmentation models exhibit impres-sive capabilities across diverse tasks, yet their performance deterioratessubstantially on medical images due to domain and sub-domain shifts.Online test-time adaptation (OTTA) helps alleviate these shifts, but ex-isting methods remain limited in effectiveness and efficiency. To thisend, we introduce Semantic-Preserving Dual-Perturbation Adaptation(SPDA), a resource-efficient OTTA framework tailored for promptablemedical segmentation. SPDA constructs diverse and robust learning sig-nals by enforcing feature and prediction consistency across two semantic-preserving perturbations: (i) a domain-adaptive low-frequency radial-spectrum perturbation that simulates task-irrelevant intra-sub-domainvariations without out-of-domain distortions, and (ii) a prompt under-sampling perturbation that reduces prompt density while preservingtarget semantics. These consistency constraints drive the optimizationof a lightweight post-encoder adapter, facilitating efficient adaptationwith minimal computational overhead. Extensive experiments on varied2D/3D medical data demonstrate that SPDA consistently outperformsstate-of-the-art OTTA baselines under box and point prompts, whileusing substantially fewer resources. Ablation studies validate each com-ponent and indicate the suitability for real-time clinical deployment.