Nickel-based　single　crystal　superalloys,　as　engine　blade　materials,　are　prone　to　fatigue　damage　due　to　repeated　startups　and　shutdowns.　Therefore,　monitoring　and　quantitatively　estimating　fatigue　cracks　are　essential　for　engineering　structures　to　ensure　safety.　In　this　study,　we　proposed　a　method　for　fatigue　crack　segmentation　and　damage　prediction　based　on　deep　learning　and　in-situ　high-temperature　scanning　electron　microscopy　(SEM).　Sequential　SEM　images　describing　the　crack　initiation　and　propagation　under　near-service　conditions　were　obtained　by　conducting　in-situ　high-temperature　fatigue　experiments.　A　fatigue　crack　dataset　with　high-quality　was　thus　constructed　for　further　dynamic　and　real-time　crack　segmentation　and　damage　assessment.　Deep　learning-based　models　were　used　to　segment　cracks　and　predict　damage　behavior　(i.e.,　crack　area,　length,　width,　and　stress　intensity　factors)　at　future　based　on　prior　damage　information.　The　short-term　and　long-term　damage　prediction　capability　were　validated　by　comparing　model　performance　when　predicting　damage　at　different　future　time　points.　Additionally,　we　compared　the　model　performance　when　predicting　damage　at　specific　time　point　based　on　varying　lengths　of　input　sequence.　Results　demonstrated　that　the　model　could　segment　cracks　and　scales　with　different　sizes　accurately.　The　model　performed　well　in　short-term　damage　prediction.　The　long-term　predictive　performance　showed　decrease　than　that　of　short-term,　which　could　be　improved　by　feeding　long　length　of　input　sequence.　The　proposed　approach　demonstrates　the　feasibility　and　effectiveness　of　deep　learning-based　crack　segmentation　and　damage　prediction,　which　facilitates　the　move　toward　real-time　analysis　and　rapid　diagnosis　of　material　damage　in　the　future.　©　2024