Multiple cosmological observables, such as the galaxy overdensity or cosmic shear, consist of fields sampled at the discrete positions of astrophysical sources. Recent work has presented methods to estimate the angular power spectra of such fields, avoiding the construction of pixelated sky maps and the finite-resolution effects associated with them. In this work, we present a method to estimate the disconnected (also known as “Gaussian”) covariance of these angular power spectra, addressing subtle effects such as the effective area overlap between different catalogue-based fields and the additional Poisson-like variance arising from the discrete nature of the catalogues. The method relies on the so-called Narrow-Kernel Approximation to account for the contribution of distinct source pairs to the estimator, while including the noise-like contributions from self-pairs exactly. We explicitly compare this approach with a brute-force method that can produce the exact covariance for sparse samples, and validate it against simulations. We show that the method is accurate in realistic scenarios, spanning both dense and noise-dominated datasets (e.g., cosmic shear) and sparse, noise-dominated observables (e.g., fast radio bursts). As a side result, we find that catalogue-based spherical transforms are faster than pixel-based transforms if $N_{\rm sources} \lesssim 10N_{\rm pixels}$. The method is implemented in the public code NaMaster.