Precision　diagnosis　and　treatment　are　blending　outcomes　of　machine　learning　and　the　Internet　of　Medical　Things　(IoMT).　In　the　diabetes　treatment,　a　medical　center　acts　as　a　medical　service　provider　(MSP)　with　patients　data　from　IoMT　devices.　The　MSP　calculates　the　accurate　dosage　by　importing　the　health　index　data　into　a　corresponding　decision-making　model.　However,　the　outsourcing　unprotected　patient　data　directly　to　the　MSP　suffers　privacy　leakage.　In　this　paper,　we　propose　a　privacy-preserving　optimal　insulin　dosing　decision　in　the　IoMT　system　(PIDM)　to　assist　doctors　in　their　decision-making　with　the　patients　privacy.　To　achieve　practicality　and　confidentiality　simultaneously,　we　design　a　series　of　secure　and　efficient　interactive　protocols　depending　on　additive　secret　sharing　to　perform　in　one　stage　of　DQN,　namely,　optimal　decision　making.　Contrasted　to　the　most　relevant　schemes,　no　additional　trusted　party　is　needed　in　our　PIDM,　which　makes　our　system　more　practical　and　efficient.　The　security　of　PIDM　is　testified,　meanwhile,　the　system　effectiveness,　and　the　overall　efficiency　of　PIDM　is　demonstrated　through　theoretical　analysis　and　simulation　experiments.