Medical image segmentation remains a cornerstone for precision diagnosis and therapeutic planning in clinical workflows. However, deep learning models trained on development datasets often suffer from limited generalizability, exhibiting poor performance when deployed across different clinical centers due to domain shifts (such as variations in imaging protocols). It raises privacy concerns when sensitive patient data needs to be shared across institutions. Meanwhile, the increasing prevalence of high-resolution medical images (e.g., ultra-wide-field retinal scans) exacerbates the challenge of manual annotation, which is labor-intensive. To address the need for efficient and privacy-preserving model adaptation and deployment, we propose a Point-Driven Source-Free Active Domain Adaptation Framework. This framework enables significant performance improvement in high-resolution medical image segmentation across multiple datasets. Notably, it achieves this without accessing source domain data, relying only on annotating a minimal number of pixels. By bridging the gap between algorithm generalization and clinical practicality, our framework offers a privacy-compliant solution for deploying medical image segmentation models across heterogeneous healthcare environments, highlighting its potential for cost-effective and secure precision medicine. Furthermore, we provide our insights into the limitations of current paradigms and outline promising directions for future research in efficient medical AI.