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Three decades of routine satellite altimetry have provided an ice-sheet-wide, near-continuous observational record of polar topography, offering unparalleled insights into ice sheet elevation change. The ability of CryoSat-2, Sentinel-3 and ICESat-2 to simultaneously and continually monitor Earth’s ice surfaces is critical towards understanding the ongoing and future imbalance of the icy continents in a changing climate. To capitalise fully on the vast quantity of altimetry data from numerous coincident missions, it is important for robust, consistent and traceable uncertainties to be provided alongside measurements of ice sheet elevation. Such information is crucial for the successful combination of measurements across missions and to enable their use in downstream applications, such as the constraint of numerical ice sheet models. At present, such uncertainties are largely absent from existing ice sheet elevation products, and for the subset of products where uncertainties are provided, there is neither a standardised approach to uncertainty generation nor a method to evaluate their robustness.Here, we present a new framework for generating and evaluating the performance of uncertainties for altimetry-based ice sheet elevation measurements and provide a comprehensive assessment of uncertainty generation for ice-sheet-wide altimetry-based ice sheet elevation datasets. Overall, we find that calculating uncertainty as a parameterisation of topographic complexity (characterised by surface slope and roughness) and measurement quality (characterised by backscattered power and coherence) improves performance relative to solutions that use fewer co-variates. Ultimately, the framework presented here will enable the systematic characterisation of ice-sheet-wide elevation uncertainties associated with historical, current and future polar radar altimeter missions, which will be particularly important as the portfolio of polar radar altimeters continues to grow with the planned launch of the Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) in 2027. Such uncertainty information will aid the successful amalgamation of the vast array of multi-mission altimetry measurements using techniques such as Kalman Filtering and improve the constraint of numerical ice sheet models, which will in turn enable more refined estimations of current and future ice sheet mass balance and global sea-level rise.