Accurate Arctic surface current estimates are needed to quantify freshwater redistribution, upper-ocean connectivity, and Arctic–North Atlantic exchanges, but validating them is difficult given sparse observations, sea-ice cover, and overlapping dynamical processes. This study evaluates six widely used products against drogued drifters from the Global Drifter Program (2011–2021): AVISO altimetry-derived geostrophic currents, NeurOST machine-learning reconstructions, GlobCurrent and OSCAR multi-observational/mixed-layer total currents, and the GLORYS and TOPAZ reanalyses. Skill is assessed with complementary Eulerian point-wise metrics and Lagrangian trajectory diagnostics, including a Lagrangian Uncertainty Quantification (LUQ) framework. Eulerian results show marked regional and seasonal contrasts. NeurOST and OSCAR agree best with drifter velocities, GlobCurrent and GLORYS show intermediate skill, TOPAZ has weaker near-surface speed agreement, and AVISO underestimates current intensity and variability. Products incorporating additional observational constraints, wind-driven contributions, or mixed-layer dynamics outperform altimetry-only geostrophic estimates, especially in regions shaped by mesoscale variability, bathymetry, sea ice, and narrow gateways. The Lagrangian analysis shows that good local velocity agreement does not guarantee realistic transport: small velocity differences accumulate along trajectories, producing large deviations in pathways, retention, and connectivity. LUQ diagnostics indicate part of this mismatch reflects intrinsic transport variability and initial-condition sensitivity rather than product error alone. The authors conclude that Eulerian validation must be complemented by Lagrangian, uncertainty-aware diagnostics for studies of freshwater pathways, tracer transport, and Arctic connectivity.