Vision-language models (VLMs) have achieved strong performance on visual question answering (VQA). To mitigate individual hallucinations and blind spots, aggregating diverse perspectives via multi-agent collaboration has emerged as a promising paradigm. While this approach has shown great success in textual QA, its potential in the multimodal domain remains under-explored. Existing multi-agent VQA methods predominantly adapt text-centric protocols, focusing on textual discussions while ignoring the alignment of visual information. In this work, we reveal a key insight: answer-level agreement is insufficient for reliable multi-agent VQA; *aligned visual evidence*—shared support from the image regions agents rely on—is essential for trustworthy consensus. To leverage this insight, we propose **EAGLE** (**E**vidence-**A**ligned **G**rounded mu**L**ti-agent r**E**asoning), a training-free evidence-centered framework for coordinating multiple VLM agents. **EAGLE** explicitly exposes each agent’s grounding regions as visual evidence, enables mutual verification over the evidence, and uses evidence consistency to guide final decision-making. Experiments on six VQA benchmarks show that **EAGLE** achieves best average performance across domains while remaining lightweight, interpretable, and practical for deployment.