Multi-agent large language model systems offer a promising paradigm for multidisciplinary medical decision support by integrating heterogeneous expert perspectives. However, existing frameworks often rely on loosely structured interactions, where disagreement is insufficiently diagnosed and discussion may collapse into superficial consensus or parallel opinion aggregation. We propose Structured Adaptive Multi-disciplinary Collaboration (SAMAC), a structured and strategy-aware multi-agent framework for adaptive MDT-style medical reasoning. SAMAC introduces Structured Deliberation to detect opinion disagreement and majority not confident, along with a Consensus Purification mechanism that transforms disagreement into constructive and collaborative refinement. When disagreement arises, SAMAC adaptively selects between Self-refinement and Counter-questioning to enable evidence-grounded negotiation. Experiments on three medical datasets—RADCURE, HANCOCK, and OSCCPI—show that SAMAC consistently improves overall performance and collaborative gains over existing multi-agent baselines. In particular, SAMAC improves the Average Score by 4.29 points on the RADCURE dataset and achieves higher Relative Self-Correction Scores (RSS), indicating more effective resolution of disagreement and stable collective decisions. These results highlight the importance of controlled and diverse interaction dynamics in multi-agent medical reasoning. Code will be released upon publication.

Medical decision-making often involves integrating knowledge from multiple clinical specialties, typically achieved through multidisciplinary teams. Inspired by this collaborative process, recent work has leveraged large language models (LLMs) in multi-agent collaboration frameworks to emulate expert teamwork. While these approaches improve reasoning through agent interaction, they are limited by static, pre-assigned roles, which hinder adaptability and dynamic knowledge integration. To address these limitations, we propose KAMAC, a Knowledge-driven Adaptive Multi-Agent Collaboration framework that enables LLM agents to dynamically form and expand expert teams based on the evolving diagnostic context. KAMAC begins with one or more expert agents and then conducts a knowledge-driven discussion to identify and fill knowledge gaps by recruiting additional specialists as needed. This supports flexible, scalable collaboration in complex clinical scenarios, with decisions finalized through reviewing updated agent comments. Experiments on two real-world medical benchmarks demonstrate that KAMAC significantly outperforms both single-agent and advanced multi-agent methods, particularly in complex clinical scenarios (i.e., cancer prognosis) requiring dynamic, cross-specialty expertise. Our code is publicly available at: https://github.com/XiaoXiao-Woo/KAMAC.