In　recent　years,　supervised　learning　using　convolutional　neural　networks　(CNN)　has　served　as　a　benchmark　for　various　medical　image　segmentation　and　classification.　However,　supervised　learning　deeply　relies　on　large-scale　annotated　data,　which　is　expensive,　time-consuming,　and　even　impractical　to　acquire　in　medical　imaging　applications.　Moreover,　effective　utilization　of　annotation　resources　might　not　always　be　feasible　during　the　annotation　process.　To　optimize　the　utilization　of　annotation　resources,　a　proposed　active　learning　framework　is　introduced　that　is　applicable　to　both　2D　and　3D　segmentation　and　classification　tasks.　This　framework　aims　to　reduce　annotation　costs　by　selecting　more　valuable　samples　for　annotation　from　the　pool　of　unlabeled　data.　Based　on　the　perturbation　consistency,　we　apply　different　perturbations　to　the　input　data　and　propose　a　perturbation　consistency　evaluation　module　to　evaluate　the　consistency　among　predictions　when　applying　different　perturbations　to　the　data.　Subsequently,　we　rank　the　consistency　of　each　data　and　select　samples　with　lower　consistency　as　high-value　candidates.　These　selected　samples　are　prioritized　for　annotation.　We　extensively　validated　our　proposed　framework　on　three　publicly　available　and　challenging　medical　image　datasets,　Kvasir　Dataset,　COVID-19　Infection　Segmentation　Dataset,　and　BraTS2019　Dataset.　The　experimental　results　demonstrate　that　our　proposed　framework　can　achieve　significantly　improved　performance　with　fewer　annotations　in　2D　classification　and　segmentation　and　3D　segmentation　tasks.　The　proposed　framework　enables　more　efficient　utilization　of　annotation　resources　by　annotating　more　representative　samples,　thus　enhancing　the　model＇s　robustness　with　fewer　annotation　costs.