Medical image annotation is a complex and crucial process that requires high precision due to the significant implications for patient outcomes and healthcare advancements. Unlike annotations for non-medical images, medical data involves larger file sizes, diverse formats, and stringent regulatory compliance, necessitating meticulous labeling by experts to train computer vision, machine learning, and AI models effectively. These annotations are integral for innovations in diagnosis and treatment, spanning fields such as radiology, gastroenterology, histology, and cancer detection, where accurate labeling can enhance early detection and treatment plans. The process involves sourcing, preparing, and annotating datasets while ensuring data security and regulatory compliance, with options ranging from open-source to third-party annotation tools. Encord, for example, offers a comprehensive suite for medical image annotation, emphasizing quality assurance, collaborative workflows, and AI-assisted labeling, demonstrating its applicability in real-world medical projects that significantly reduce annotation time and improve experimental efficiency.