Large Language Models (LLMs) offer a scalable approach to fact-checking, but their parametric knowledge remains unreliable for complex, specialized claims. Existing solutions unconditionally perform verification using Retrieval-Augmented Generation (RAG), which is computationally expensive and susceptible to performance-limiting noise. We propose an adaptive fact-checking pipeline that invokes RAG only when the LLM’s uncertainty surpasses a calibrated threshold. We introduce two UQ methods: (1) Adversarial Consistency Evaluation (ACE), a novel approach that detects model uncertainty by assessing its logical consistency, and (2) a trained ensemble that combines the strengths of several whitebox UQ methods and ACE. When evaluated on the SciFact and QuanTemp benchmarks, our adaptive pipeline preserves at least 88% of the top-performing baseline’s accuracy. Simultaneously, our approach reduces retrieval frequency and token consumption by up to 55.3% and 37.1%, respectively, consistently outperforming decomposition-based RAG baselines at a fraction of the cost.