  The ``Residual-to-Residual DNN series for high-Dynamic range imaging'' (R2D2)
approach was recently introduced for Radio-Interferometric (RI) imaging in
astronomy. R2D2's reconstruction is formed as a series of residual images,
iteratively estimated as outputs of Deep Neural Networks (DNNs) taking the
previous iteration's image estimate and associated data residual as inputs. In
this work, we investigate the robustness of the R2D2 image estimation process,
by studying the uncertainty associated with its series of learned models.
Adopting an ensemble averaging approach, multiple series can be trained,
arising from different random DNN initializations of the training process at
each iteration. The resulting multiple R2D2 instances can also be leveraged to
generate ``R2D2 samples'', from which empirical mean and standard deviation
endow the algorithm with a joint estimation and uncertainty quantification
functionality. Focusing on RI imaging, and adopting a telescope-specific
approach, multiple R2D2 instances were trained to encompass the most general
observation setting of the Very Large Array (VLA). Simulations and real-data
experiments confirm that: (i) R2D2's image estimation capability is superior to
that of the state-of-the-art algorithms; (ii) its ultra-fast reconstruction
capability (arising from series with only few DNNs) makes the computation of
multiple reconstruction samples and of uncertainty maps practical even at large
image dimension; (iii) it is characterized by a very low model uncertainty.
