Fighting medical disinformation in the era of the global pandemic is an increasingly important problem. As of today, automatic systems for assessing the credibility of medical information do not offer sufficient precision to be used without human supervision, and the involvement of medical expert annotators is required. Thus, our work aims to optimize the utilization of medical experts’ time. We use the dataset of sentences taken from online lay medical articles. We propose a general framework for filtering medical statements that do not need to be manually verified by medical experts. The results show the gain in fact-checking performance of expert annotators on capturing misinformation by the factor of 2.2 on average. In other words, our framework allows medical experts to fact-check and identify over two times more non-credible medical statements in a given time interval without applying any changes to the annotation flow.