Dense retrievers bridge vocabulary gaps but suffer from semantic drift, ranking topically similar passages that are not actually relevant to the query. Sparse retrievers like BM25 are generally less prone to such false positives but miss paraphrases. We observe that these failure modes are complementary, with relevant passages concentrated in regions where both retrievers score highly, while single-retriever confidence proves unreliable. Building on this insight, we propose AgreRank, a re-ranking method that leverages sparse-dense consensus for query expansion. AgreRank identifies anchor documents where both retrievers agree, expands the query through these verified anchors, and applies geometric gating to suppress drift from the original intent. On TREC Deep Learning 2021 and 2022, AgreRank improves over Dense+CE by 3.4--5.7% and over RRF+CE by 3.7--5.6% under identical retrieval and reranking components, achieving nDCG@10 of 0.706 and 0.627 with a lightweight 33M-parameter cross-encoder. These gains require no additional parameters or training, demonstrating that more effective use of retrieval signals offers a practical alternative to model scaling.