Medical image segmentation is a critical process in healthcare that involves extracting regions of interest from medical images, such as CT scans, MRIs, and X-rays, to enhance the accuracy and efficiency of computer vision models used in medical diagnostics. This technique is pivotal for training AI models by providing precise labeling and annotation of large datasets, thereby improving the models' ability to identify health issues that medical professionals might miss. Various segmentation methods, ranging from traditional to advanced deep learning techniques, are applied in fields like radiology, gastroenterology, histology, and cancer detection to facilitate accurate diagnoses and treatment plans. Platforms like Encord offer AI-powered annotation tools that enable seamless collaboration among medical professionals, machine learning engineers, and annotation teams, significantly improving labeling efficiency and reducing image processing time. Encord’s capabilities have been successfully leveraged by institutions such as Stanford Medicine and King’s College London to enhance their medical imaging workflows, demonstrating the platform's impact on accelerating and automating the data labeling process in the medical field.