Cardiac modeling entails the epistemic uncertainty of the input parameters,such as bundles and chambers geometry,electrical conductivities and cell parameters,thus calling for an uncertainty quantification(UQ)analysis.Since the cardiac activation and the subsequent muscular contraction is provided by a complex electrophysiology system made of interconnected conductive media,we focus here on the fast conductivity structures of the atria(internodal pathways)with the aim of identifying which of the uncertain inputs mostly influence the propagation of the depolarization front.Firstly,the distributions of the input parameters are calibrated using data available from the literature taking into account gender differences.The output quantities of interest(QoIs)of medical relevance are defined and a set of metamodels(one for each QoI)is then trained according to a polynomial chaos expansion(PCE)in order to run a global sensitivity analysis with non-linear variance-based Sobol\'indices with confidence intervals evaluated through the bootstrap method.The most sensitive parameters on each QoI are then identified for both genders showing the same order of importance of the model inputs on the electrical activation.Lastly,the probability distributions of the QoIs are obtained through a forward sensitivity analysis using the same trained metamodels.It results that several input parameters—including the position of the internodal pathways and the electrical impulse applied at the sinoatrial node—have a little influence on the QoIs studied.Vice-versa the electrical activation of the atrial fast conduction system is sensitive on the bundles geometry and electrical conductivities that need to be carefully measured or calibrated in order for the electrophysiology model to be accurate and predictive.