In　mobile　edge　computing　(MEC)　systems,　unmanned　aerial　vehicles　(UAVs)　facilitate　edge　service　providers　(ESPs)　offering　flexible　resource　provisioning　with　broader　communication　coverage　and　thus　improving　the　Quality　of　Service　(QoS).　However,　dynamic　system　states　and　various　traffic　patterns　seriously　hinder　efficient　cooperation　among　UAVs.　Existing　solutions　commonly　rely　on　prior　system　knowledge　or　complex　neural　network　models,　lacking　adaptability　and　causing　excessive　overheads.　To　address　these　critical　challenges,　we　propose　the　DisOff,　a　novel　profit-aware　cooperative　offloading　framework　in　UAV-enabled　MEC　with　lightweight　deep　reinforcement　learning　(DRL).　First,　we　design　an　improved　DRL　with　twin　critic-networks　and　delay　mechanism,　which　solves　the　$Q$　-value　overestimation　and　high　variance　and　thus　approximates　the　optimal　UAV　cooperative　offloading　and　resource　allocation.　Next,　we　develop　a　new　multiteacher　distillation　mechanism　for　the　proposed　DRL　model,　where　the　policies　of　multiple　UAVs　are　integrated　into　one　DRL　agent,　compressing　the　model　size　while　maintaining　superior　performance.　Using　the　real-world　datasets　of　user　traffic,　extensive　experiments　are　conducted　to　validate　the　effectiveness　of　the　proposed　DisOff.　Compared　to　benchmark　methods,　the　DisOff　enhances　ESP　profits　while　reducing　the　DRL　model　size　and　training　costs.