Question　Answering　(QA)　has　been　successfully　applied　in　scenarios　of　human-computer　interaction　such　as　chatbots　and　search　engines.　However,　for　the　specific　biomedical　domain,　QA　systems　are　still　immature　due　to　expert-annotated　datasets　being　limited　by　category　and　scale.　In　this　paper,　we　present　MLEC-QA,　the　largest-scale　Chinese　multi-choice　biomedical　QA　dataset,　collected　from　the　National　Medical　Licensing　Examination　in　China.　The　dataset　is　composed　of　five　subsets　with　136,236　biomedical　multi-choice　questions　with　extra　materials　(images　or　tables)　annotated　by　human　experts,　and　first　covers　the　following　biomedical　sub-fields:　Clinic,　Stomatology,　Public　Health,　Traditional　Chinese　Medicine,　and　Traditional　Chinese　Medicine　Combined　with　Western　Medicine.　We　implement　eight　representative　control　methods　and　open-domain　QA　methods　as　baselines.　Experimental　results　demonstrate　that　even　the　current　best　model　can　only　achieve　accuracies　between　40%　to　55%　on　five　subsets,　especially　performing　poorly　on　questions　that　require　sophisticated　reasoning　ability.　We　hope　the　release　of　the　MLEC-QA　dataset　can　serve　as　a　valuable　resource　for　research　and　evaluation　in　open-domain　QA,　and　also　make　advances　for　biomedical　QA　systems.　©　2021　Association　for　Computational　Linguistics