The　existing　predictive　failure　probability　(PFP)-based　global　sensitivity　analysis　methods　may　get　stuck　in　practical　application　due　to　the　large　amount　of　calculation　or　insufficient　precision.　Therefore,　a　novel　singleloop　estimation　method　is　proposed　in　this　paper　to　effectively　estimate　PFP-based　global　sensitivity　measures.　Firstly,　by　introducing　standard　normal　auxiliary　variables　and　equivalent　probability　transformation,　the　overall　uncertainty　of　each　input　variable　is　decomposed　into　its　inherent　uncertainty　and　distribution　parameter　uncertainty.　Then,　Bayes　theorem　-based　formulations　are　derived　for　three　PFP-based　global　sensitivity　measures,　where　only　the　unconditional　PFP　and　the　conditional　marginal　and　joint　distributions　of　standard　normal　auxiliary　variables　and　distribution　parameters　need　to　be　known.　These　items　can　be　evaluated　simultaneously　using　only　one　set　of　functional　calls.　Finally,　adaptive　Kriging　model　is　embedded　into　the　proposed　method　in　place　of　the　time　-demanding　performance　function　to　further　enhance　computational　efficiency.　The　effectiveness　of　the　proposed　methods　is　validated　through　four　benchmark　reliability　problems　asnd　one　engineering　problem.