Magnetic interatomic potentials need to account for coupled lattice and spin degrees of freedom, yet constructing reliable training sets remains costly because noncollinear first-principles labels are expensive. Active learning can mitigate this cost, provided that the uncertainty estimate is physically meaningful for the magnetic-response targets that drive spin reorientation. Here we introduce Equivariant Evidential Deep Learning for Spin-Lattice Potentials (e2SLP), a magnetic uncertainty-learning method that formulates the projected spin-force likelihood and the corresponding epistemic uncertainty in the tangent plane orthogonal to the local spin direction. This geometry is consistent with the constrained-moment labels, which contain no independent radial response, and yields a structure-level projected-spin-force uncertainty score for candidate selection. Using bulk BiFeO3 and monolayer CrTe2 as benchmark systems, we show that this score correlates strongly with prediction errors and that the full e2SLP selection, training, and fine-tuning workflow lowers energy, force, and projected spin-force errors relative to random sampling. For monolayer CrTe2, round-by-round comparisons show accuracy competitive with a four-model committee, while a four-seed ablation separates the contributions of uncertainty-guided selection and e2SLP training followed by fine-tuning. e2SLP thus provides a single-model tangent-plane uncertainty criterion for data-efficient construction of magnetic machine-learning interatomic potentials.