This paper introduces a computationally efficient extrema approach for the probabilistic predictions of creep in finite element analysis (FEA). Component-level probabilistic simulations are needed to assess the reliability and safety of high-temperature components. Full-scale probabilistic creep models in FEA are computationally expensive, requiring many hundreds of simulations to replicate the uncertainty of component failure. Extrema are conditions at which the values of a function are the largest or the smallest. In this study, an extrema approach is proposed. In the extrema approach, full-scale probabilistic simulations are completed in one-dimensional across a wide range of stresses, the results are processed, and extrema conditions are extracted. The extrema conditions alone are applied in two-/three-dimensional FEA to predict the mean and range of creep failure. The probabilistic Sinh model, calibrated for alloy 304 stainless steel, is selected. The sources of uncertainty (i.e., test condition, pre-existing damage, and model constants) are evaluated and probability distribution functions sampling are performed via Monte Carlo method. The extrema conditions considered include the range of creep ductility, rupture, and area under creep curves. The predicted creep response for one- and two-dimensional model shows agreement with the experimental data. It is determined that extrema approach will significantly reduce the computational cost of probabilistic creep predictions in FEA.