Retrieval-Augmented Generation (RAG) provides external knowledge support for large language models (LLMs) in medical applications, but retrieved contexts often contain noisy or conflicting evidence that can degrade reasoning. We observe that when internal and external knowledge disagree, models systematically prefer external citations, inadvertently injecting retrieval noise. Our analyses further show that only a subset of retrieved citations consistently improves outcomes; these effective citations exhibit markedly lower token-level entropy, linking citation entropy to model accuracy. Building on these findings, we propose a complete pipeline consisting of a training-free multi-turn reasoning framework and a post-training methodology. The training-free framework elicits internal thought, external thought, and fusion thought, and applies conflict detection and explicit denoising for complex queries. For post-training, we distill structured supervised fine-tuning (SFT) data and apply GRPO with an entropy-based citation reward that encourages selective citation of beneficial external knowledge while penalizing noisy citations. Experiments across diverse benchmarks demonstrate consistent gains in noise-resistant medical reasoning, with larger improvements on harder cases.