Large　language　models　(LLMs)　are　increasingly　adopted　in　medical　question　answering　(QA)　scenarios.　However,　LLMs　have　been　proven　to　generate　hallucinations　and　nonfactual　information,　undermining　their　trustworthiness　in　high-stakes　medical　tasks.　Conformal　Prediction　(CP)　is　now　recognized　as　a　robust　framework　within　the　broader　domain　of　machine　learning,　offering　statistically　rigorous　guarantees　of　marginal　(average)　coverage　for　prediction　sets.　However,　the　applicability　of　CP　in　medical　QA　remains　to　be　explored.　To　address　this　limitation,　this　study　proposes　an　enhanced　CP　framework　for　medical　multiple-choice　question　answering　(MCQA)　tasks.　The　enhanced　CP　framework　associates　the　non-conformance　score　with　the　frequency　score　of　the　correct　option.　The　framework　generates　multiple　outputs　for　the　same　medical　query　by　leveraging　self-consistency　theory.　The　proposed　framework　calculates　the　frequency　score　of　each　option　to　address　the　issue　of　limited　access　to　the　model＇s　internal　information.　Furthermore,　a　risk　control　framework　is　incorporated　into　the　enhanced　CP　framework　to　manage　task-specific　metrics　through　a　monotonically　decreasing　loss　function.　The　enhanced　CP　framework　is　evaluated　on　three　popular　MCQA　datasets　using　off-the-shelf　LLMs.　Empirical　results　demonstrate　that　the　enhanced　CP　framework　achieves　user-specified　average　(or　marginal)　error　rates　on　the　test　set.　Moreover,　the　results　show　that　the　test　set＇s　average　prediction　set　size　(APSS)　decreases　as　the　risk　level　increases.　It　is　concluded　that　it　is　a　promising　evaluation　metric　for　the　uncertainty　of　LLMs.